Geomechanical Principles Of Hydraulic Fracturing Method In Unconventional Gas Reservoirs
Bibliographic record
Abstract
Unconventional gas production from shale formation is not new to oil and gas experts worldwide. But our research work was built around hydraulic fracturing technique with focus on the Perkins Kern-Nordgren (PKN) 1972 hydraulic fracturing model(s). It is a very robust and flexible model that can be used on two major shale reservoirs (with the assumption of a fixed height and fracture fluid pressure). The essence was to compare detailed geo-mechanical parameters extracted from wire-line logs with Perkin-C model to select the right well as candidate for simulation. It aided in the prediction production of shale gas from tight shale formations. These also helped in reviewing safe and economical ways of obtaining clean energy sources. Based on similarities in well and formation properties our research team subjected IDJE-2 well (located in the Agbada shale Formation of Niger Delta, Nigeria) to various conditions, equations and assumptions proposed by the study model while also validating our results with the PENOBSCOT L-30 well, located in Canada (with existing profound results from stimulations). The PENOBSCOT L-30 well (Case 1) and IDJE-2 well (Case 2) were both subjected to same conditions, equations and assumptions as applicable to the study model to enable us compare and evaluate stimulation performances. But both cases tend to react differently. However the fluid behavior at constant injection time increases at about 99.64%. Whereas, the maximum width at wellbore shows that a constant increase of fracture width will yield an increase in propant permeability, tensile strength and Poisson’s ratio for Case 1 & 2. Our research results show how rock properties can affect fracture geometry and expected production rates from stimulated shale reservoir formations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".